Many of you might take this for granted, and I know it seems amazing today, but I when first started teaching, our access to scientific literature was pretty limited. I could go to the UW and use Grateful Med to search Medline, but we didn't have anything like it at my college and web browsers, like Mosaic, had yet to be invented. So, when I first started giving workshops for teachers on biotechnology and the world of the web, many were quite surprised to find out about the PubMed database.
Since PubMed is (to me) one of the best resources to ever come along, I think we should explore it a bit in these next few sessions.
Here's your assignment:
You are going to find a plant gene that's related to a specific trait. (FYI: Some traits are controlled by one gene, some by multiple genes, but we are not going to worry about that right now). Let your mind wander for a few minutes and think about useful or interesting traits that a plant might have - like the ability to make blue flowers - or the ability to grow in cold weather, make crystals, or produce poisons.
1. Pick a plant trait that you find interesting.
2. Write down some words that describe the trait and some synonyms for those words.
3. Open this link to the NCBI in a different window, and click the term PubMed (upper left hand corner).
4. Use your terms to do some quick searches and see how many articles you get that are related to your trait. Don't try and read them all! But - do scan a few titles. Do any titles look promising?
Here's one question to keep in mind: If you find a plant gene that might be related to the trait you chose, how will you demonstrate it?
If you like, take a look at one of our tutorials on searching PubMed.
We have a quick one here and a longer one here. The longer one looks like it's about cancer but it's really about PubMed.
TTFN!
technorati tags: PubMed, digital biology, using databases
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When I started in science as an undergrad, the only way to access the scientific literature was by leafing through large bound volumes of Index Medicus. We have it easy today. My new wish is that we could search the full-text scientific literature using word proximity searches like Lexis/Nexis, and not just keywords and abstracts.